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Data assimilation of atmospheric observations traditionally relies on variational and Kalman filter methods.
Machine Learning for Model Error Inference and Correction
M. Bonavita and P. Laloyaux · 1942
Earlier work this paper cites.
Learning Variational Data Assimilation Models and Solvers
R. Fablet, B. Chapron, L. Drumetz, E. Mémin, O. Pannekoucke, and F. Rousseau · 1942
Earlier work this paper cites.
Building tangent-linear and adjoint models for data assimilation with neural networks
S. Hatfield, M. Chantry, P. Dueben, P. Lopez, A. Geer, and T. Palmer · 1942
Earlier work this paper cites.
Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System
P. Laloyaux, T. Kurth, P. D. Dueben, and D. Hall · 1942
Earlier work this paper cites.
Improving Data-Driven Global Weather Prediction Using Deep Convolutional Neural Networks on a Cubed Sphere
J. A. Weyn, D. R. Durran, and R. Caruana · 1942
Earlier work this paper cites.
Computing the Ensemble Spread From Deterministic Weather Predictions Using Conditional Generative Adversarial Networks
R. Brecht and A. Bihlo · 1944
Earlier work this paper cites.
Can Artificial Intelligence-Based Weather Prediction Models Simulate the Butterfly Effect?
T. Selz and G. C. Craig · 1944
Earlier work this paper cites.
Quasi-geostrophic motions in the equatorial area
T. Matsuno · 1966
Earlier work this paper cites.
A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances
R. N. Bannister · 1970
Earlier work this paper cites.
Some simple solutions for heat-induced tropical circulation
A. E. Gill · 1980
Earlier work this paper cites.
Spectral Representation of Three-Dimensional Global Data by Expansion in Normal Mode Functions
A. Kasahara and K. Puri · 1980
Earlier work this paper cites.
Analysis methods for numerical weather prediction
A. C. Lorenc · 1986
Earlier work this paper cites.
A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics
R. N. Bannister · 1996
Earlier work this paper cites.
Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures
K. E. Trenberth, G. W. Branstator, D. Karoly, A. Kumar, N.-C. Lau, and C. Ropelewski · 1998
Earlier work this paper cites.
Atmospheric modeling, data assimilation, and predictability , volume 54
E. Kalnay · 2002
Earlier work this paper cites.
Background error covariance modelling
M. Fisher · 2003
Earlier work this paper cites.
Variational bias correction of radiance data in the ECMWF system
D. P. Dee · 2004
Earlier work this paper cites.
Variational data assimilation in the tropics: The impact of a background-error constraint
N. Žagar, N. Gustafsson, and E. Källén · 2004
Earlier work this paper cites.
A reduced-order strategy for 4D-Var data assimilation
C. Robert, S. Durbiano, E. Blayo, J. Verron, J. Blum, and F. X. Le Dimet · 2005
Earlier work this paper cites.
Balanced tropical data assimilation based on a study of equatorial waves in ECMWF short-range forecast errors
N. Žagar, E. Andersson, and M. Fisher · 2005
Earlier work this paper cites.
V. Böhm and U. Seljak · 2006
Earlier work this paper cites.
Four-dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements
T. Chai, G. R. Carmichael, Y. Tang, A. Sandu, M. Hardesty, P. Pilewskie, S. Whitlow, E. V. Browell, M. A. Avery, P. Nédélec, J. T. Merrill, A. M. Thompson, and E. Williams · 2007
Earlier work this paper cites.
Combining the mid-latitudinal and equatorial mass / wind balance relationships in global data assimilation
H. Körnich and E. Källén · 2007
Earlier work this paper cites.
A hybrid approach to estimating error covariances in variational data assimilation
H. Cheng, M. Jardak, M. Alexe, and A. Sandu · 2010
Earlier work this paper cites.
Ensemble of data assimilations at ECMWF
L. Isaksen, M. Bonavita, R. Buizza, M. Fisher, J. Haseler, M. Leutbecher, and L. Raynaud · 2010
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On the use of EDA background error variances in the ECMWF 4D-Var
M. Bonavita, L. Isaksen, and E. Hólm · 2012
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Linearized physics for data assimilation at ECMWF
M. Janisková and P. Lopez · 2013
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Data assimilation: Making Sense of Observations
W. A. Lahoz and P. Schneider · 2014
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Deep Learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
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Adam: A Method for Stochastic Optimization, 2017
D. P. Kingma and J. Ba · 2017
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Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro
V. A. de Almeida, H. F. de Campos Velho, G. B. França, and N. F. F. Ebecken · 2022
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Forecasting Global Weather with Graph Neural Networks, 2022
R. Keisler · 2022
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Auto-Encoding Variational Bayes, 2022
D. P. Kingma and M. Welling · 2022
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Combining data assimilation and machine learning to estimate parameters of a convective-scale model
S. Legler and T. Janjić · 2022
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Machine Learning Emulation of 3D Cloud Radiative Effects
D. Meyer, R. J. Hogan, P. D. Dueben, and S. L. Mason · 2022
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All-sky satellite data assimilation at operational weather forecasting centres
A. J. Geer, K. Lonitz, P. Weston, M. Kazumori, K. Okamoto, Y. Zhu, E. H. Liu, A. Collard, W. Bell, S. Migliorini, P. Chambon, N. Fourrié, M. J. Kim, C. Köpken-Watts, and C. Schraff · 2018
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An intermediate-complexity model for four-dimensional variational data assimilation including moist processes
Z. Zaplotnik, N. Žagar, and N. Gustafsson · 2018
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Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother
S. W. Canchumuni, A. A. Emerick, and M. A. C. Pacheco · 2019
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An introduction to variational autoencoders
D. P. Kingma and M. Welling · 2019
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Modal Decomposition of the Global Response to Tropical Heating Perturbations Resembling MJO
K. Kosovelj, F. Kucharski, F. Molteni, and N. Žagar · 2019
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Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization
M. Bocquet, J. Brajard, A. Carrassi, L. Bertino, M. Bocquet, J. Brajard, A. Carrassi, and L. Bertino · 2020
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J. Pathak, S. Subramanian, P. Harrington, S. Raja, A. Chattopadhyay, M. Mardani, T. Kurth, D. Hall, Z. Li, K. Azizzadenesheli, P. Hassanzadeh, K. Kashinath, and A. Anandkumar · 2022
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Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
Y. Zhuang, S. Cheng, N. Kovalchuk, M. Simmons, O. K. Matar, Y.-K. Guo, and R. Arcucci · 2022
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Deep Learning for Day Forecasts from Sparse Observations
M. Andrychowicz, L. Espeholt, D. Li, S. Merchant, A. Merose, F. Zyda, S. Agrawal, N. Kalchbrenner, G. Deepmind, and G. Research · 2023
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Accurate medium-range global weather forecasting with 3D neural networks
K. Bi, L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian · 2023
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On the limitations of data-driven weather forecasting models
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Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review
S. Cheng, C. Quilodran-Casas, S. Ouala, A. Farchi, C. Liu, P. Tandeo, R. Fablet, D. Lucor, B. Iooss, J. Brajard, D. Xiao, T. Janjic, W. Ding, Y. Guo, A. Carrassi, M. Bocquet, and R. Arcucci · 2023
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IFS Documentation CY48R1 - Part II: Data Assimilation
ECMWF · 2023
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Dynamical Tests of a Deep-Learning Weather Prediction Model
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Learning skillful medium-range global weather forecasting
R. Lam, A. Sanchez-Gonzalez, M. Willson, P. Wirnsberger, M. Fortunato, F. Alet, S. Ravuri, T. Ewalds, Z. Eaton-Rosen, W. Hu, A. Merose, S. Hoyer, G. Holland, O. Vinyals, J. Stott, A. Pritzel, S. Mohamed, and P. Battaglia · 2023
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ClimaX: A foundation model for weather and climate, 2023
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Weather forecasting with convolutional neural networks. Master Thesis, 2023
U. Perkan · 2023
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WeatherBench 2: A benchmark for the next generation of data-driven global weather models
S. Rasp, S. Hoyer, A. Merose, I. Langmore, P. Battaglia, T. Russel, A. Sanchez-Gonzalez, V. Yang, R. Carver, S. Agrawal, M. Chantry, Z. B. Bouallegue, P. Dueben, C. Bromberg, J. Sisk, L. Barrington, A. Bell, and F. Sha · 2023
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Flow-dependent wind extraction in strong-constraint 4D-Var
Z. Zaplotnik, N. Žagar, and N. Semane · 2023
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Balance properties of the short-range forecast errors in the ECMWF 4D-Var ensemble
N. Žagar, L. Isaksen, D. Tan, and J. Tribbia · 2033
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Deep learning for bias correction of MJO prediction
H. Kim, Y. G. Ham, Y. S. Joo, and S. W. Son · 2041
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Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
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ECMWF short-term prediction accuracy improvement by deep learning
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Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
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